


"""simple docstring"""



import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor

lowercase_							=							random.Random()
def      UpperCAmelCase     (       _lowercase :   Any			,   _lowercase :   Optional[int]=1.0			,   _lowercase :   str=None			,   _lowercase :   int=None	)						-> Tuple:


	"""simple docstring"""




	if rng is None:
		lowerCAmelCase_      							=			global_rng

	lowerCAmelCase_      							=			[]
	for batch_idx in range(shape[0]	):
		values.append([]	)
		for _ in range(shape[1]	):
			values[-1].append(rng.random() * scale	)

	return values







@require_torch
@require_torchaudio
class    __a						( unittest.TestCase							):







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase=7      ,	UpperCAmelCase=400      ,	UpperCAmelCase=2000      ,	UpperCAmelCase=24      ,	UpperCAmelCase=24      ,	UpperCAmelCase=0.0      ,	UpperCAmelCase=1_6000      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			parent
					lowerCAmelCase_      							=			batch_size
					lowerCAmelCase_      							=			min_seq_length
					lowerCAmelCase_      							=			max_seq_length
					lowerCAmelCase_      							=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					lowerCAmelCase_      							=			feature_size
					lowerCAmelCase_      							=			num_mel_bins
					lowerCAmelCase_      							=			padding_value
					lowerCAmelCase_      							=			sampling_rate
					lowerCAmelCase_      							=			return_attention_mask
					lowerCAmelCase_      							=			do_normalize







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return {
					    "feature_size": self.feature_size,
					    "num_mel_bins": self.num_mel_bins,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					    "do_normalize": self.do_normalize,
					}







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase=False      ,	UpperCAmelCase=False   ):

					'''simple docstring'''






					def _flatten(UpperCAmelCase   ):
						return list(itertools.chain(*UpperCAmelCase   )   )

					if equal_length:
						lowerCAmelCase_      							=			[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
					else:
						# make sure that inputs increase in size
						lowerCAmelCase_      							=			[
						    floats_list((x, self.feature_size)   )
						    for x in range(self.min_seq_length      ,	self.max_seq_length      ,	self.seq_length_diff   )
						]
					if numpify:
						lowerCAmelCase_      							=			[np.asarray(UpperCAmelCase   ) for x in speech_inputs]
					return speech_inputs







@require_torch
@require_torchaudio
class    __a						( __snake_case					,			unittest.TestCase							):
				lowerCamelCase     :       List[str]			       =SpeechaTextFeatureExtractor if is_speech_available() else None







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			SpeechaTextFeatureExtractionTester(self   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					self.assertTrue(np.all(np.mean(UpperCAmelCase      ,	axis=0   ) < 1E-3   )   )
					self.assertTrue(np.all(np.abs(np.var(UpperCAmelCase      ,	axis=0   ) - 1   ) < 1E-3   )   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					# create three inputs of length 800, 1000, and 1200
					lowerCAmelCase_      							=			[floats_list((1, x)   )[0] for x in range(800      ,	1400      ,	200   )]
					lowerCAmelCase_      							=			[np.asarray(UpperCAmelCase   ) for speech_input in speech_inputs]

					# Test feature size
					lowerCAmelCase_      							=			feature_extractor(UpperCAmelCase      ,	padding=UpperCAmelCase      ,	return_tensors='''np'''   ).input_features
					self.assertTrue(input_features.ndim == 3   )
					self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size   )

					# Test not batched input
					lowerCAmelCase_      							=			feature_extractor(speech_inputs[0]      ,	return_tensors='''np'''   ).input_features
					lowerCAmelCase_      							=			feature_extractor(np_speech_inputs[0]      ,	return_tensors='''np'''   ).input_features
					self.assertTrue(np.allclose(UpperCAmelCase      ,	UpperCAmelCase      ,	atol=1E-3   )   )

					# Test batched
					lowerCAmelCase_      							=			feature_extractor(UpperCAmelCase      ,	return_tensors='''np'''   ).input_features
					lowerCAmelCase_      							=			feature_extractor(UpperCAmelCase      ,	return_tensors='''np'''   ).input_features
					for enc_seq_a, enc_seq_a in zip(UpperCAmelCase      ,	UpperCAmelCase   ):
						self.assertTrue(np.allclose(UpperCAmelCase      ,	UpperCAmelCase      ,	atol=1E-3   )   )

					# Test 2-D numpy arrays are batched.
					lowerCAmelCase_      							=			[floats_list((1, x)   )[0] for x in (800, 800, 800)]
					lowerCAmelCase_      							=			np.asarray(UpperCAmelCase   )
					lowerCAmelCase_      							=			feature_extractor(UpperCAmelCase      ,	return_tensors='''np'''   ).input_features
					lowerCAmelCase_      							=			feature_extractor(UpperCAmelCase      ,	return_tensors='''np'''   ).input_features
					for enc_seq_a, enc_seq_a in zip(UpperCAmelCase      ,	UpperCAmelCase   ):
						self.assertTrue(np.allclose(UpperCAmelCase      ,	UpperCAmelCase      ,	atol=1E-3   )   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					lowerCAmelCase_      							=			[floats_list((1, x)   )[0] for x in range(800      ,	1400      ,	200   )]

					lowerCAmelCase_      							=			['''longest''', '''max_length''', '''do_not_pad''']
					lowerCAmelCase_      							=			[None, 16, None]
					for max_length, padding in zip(UpperCAmelCase      ,	UpperCAmelCase   ):
						lowerCAmelCase_      							=			feature_extractor(
						    UpperCAmelCase      ,	padding=UpperCAmelCase      ,	max_length=UpperCAmelCase      ,	return_attention_mask=UpperCAmelCase   )
						lowerCAmelCase_      							=			inputs.input_features
						lowerCAmelCase_      							=			inputs.attention_mask
						lowerCAmelCase_      							=			[np.sum(UpperCAmelCase   ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					lowerCAmelCase_      							=			[floats_list((1, x)   )[0] for x in range(800      ,	1400      ,	200   )]

					lowerCAmelCase_      							=			['''longest''', '''max_length''', '''do_not_pad''']
					lowerCAmelCase_      							=			[None, 16, None]
					for max_length, padding in zip(UpperCAmelCase      ,	UpperCAmelCase   ):
						lowerCAmelCase_      							=			feature_extractor(
						    UpperCAmelCase      ,	max_length=UpperCAmelCase      ,	padding=UpperCAmelCase      ,	return_tensors='''np'''      ,	return_attention_mask=UpperCAmelCase   )
						lowerCAmelCase_      							=			inputs.input_features
						lowerCAmelCase_      							=			inputs.attention_mask
						lowerCAmelCase_      							=			[np.sum(UpperCAmelCase   ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
						self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6   )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
						self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6   )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					lowerCAmelCase_      							=			[floats_list((1, x)   )[0] for x in range(800      ,	1400      ,	200   )]
					lowerCAmelCase_      							=			feature_extractor(
					    UpperCAmelCase      ,	padding='''max_length'''      ,	max_length=4      ,	truncation=UpperCAmelCase      ,	return_tensors='''np'''      ,	return_attention_mask=UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			inputs.input_features
					lowerCAmelCase_      							=			inputs.attention_mask
					lowerCAmelCase_      							=			np.sum(attention_mask == 1      ,	axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1]   )
					self._check_zero_mean_unit_variance(input_features[2]   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					lowerCAmelCase_      							=			[floats_list((1, x)   )[0] for x in range(800      ,	1400      ,	200   )]
					lowerCAmelCase_      							=			feature_extractor(
					    UpperCAmelCase      ,	padding='''longest'''      ,	max_length=4      ,	truncation=UpperCAmelCase      ,	return_tensors='''np'''      ,	return_attention_mask=UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			inputs.input_features
					lowerCAmelCase_      							=			inputs.attention_mask
					lowerCAmelCase_      							=			np.sum(attention_mask == 1      ,	axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
					self._check_zero_mean_unit_variance(input_features[2]   )

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape      ,	(3, 4, 24)   )

					lowerCAmelCase_      							=			[floats_list((1, x)   )[0] for x in range(800      ,	1400      ,	200   )]
					lowerCAmelCase_      							=			feature_extractor(
					    UpperCAmelCase      ,	padding='''longest'''      ,	max_length=16      ,	truncation=UpperCAmelCase      ,	return_tensors='''np'''      ,	return_attention_mask=UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			inputs.input_features
					lowerCAmelCase_      							=			inputs.attention_mask
					lowerCAmelCase_      							=			np.sum(attention_mask == 1      ,	axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
					self._check_zero_mean_unit_variance(input_features[2]   )

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape      ,	(3, 6, 24)   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					import torch

					lowerCAmelCase_      							=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					lowerCAmelCase_      							=			np.random.rand(100      ,	32   ).astype(np.floataa   )
					lowerCAmelCase_      							=			np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
						lowerCAmelCase_      							=			feature_extractor.pad([{'''input_features''': inputs}]      ,	return_tensors='''np'''   )
						self.assertTrue(np_processed.input_features.dtype == np.floataa   )
						lowerCAmelCase_      							=			feature_extractor.pad([{'''input_features''': inputs}]      ,	return_tensors='''pt'''   )
						self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					from datasets import load_dataset

					lowerCAmelCase_      							=			load_dataset('''hf-internal-testing/librispeech_asr_dummy'''      ,	'''clean'''      ,	split='''validation'''   )
					# automatic decoding with librispeech
					lowerCAmelCase_      							=			ds.sort('''id'''   ).select(range(UpperCAmelCase   )   )[:num_samples]['''audio''']

					return [x["array"] for x in speech_samples]







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			np.array([
					    -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
					    -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
					    -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
					]   )
					# fmt: on

					lowerCAmelCase_      							=			self._load_datasamples(1   )
					lowerCAmelCase_      							=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					lowerCAmelCase_      							=			feature_extractor(UpperCAmelCase      ,	return_tensors='''pt'''   ).input_features
					self.assertEquals(input_features.shape      ,	(1, 584, 24)   )
					self.assertTrue(np.allclose(input_features[0, 0, :30]      ,	UpperCAmelCase      ,	atol=1E-4   )   )



"""simple docstring"""



from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class    __a						:
				pass